A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Open accessAbstract
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
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Al-Garadi et al. [32] and Pakalapati et al. [33] provide strategic assessments of using AI/ML for threat detection and automated decision-making within DevSecOps pipelines, signalling the start of a broader movement toward intelligent, data-driven security automation.
“Al-Garadi et al. [32] and Pakalapati et al. [33] provide strategic assessments of using AI/ML for threat detection and automated decision-making within DevSecOps pipelines, signalling the start of a broader movement toward intelligent, data-driven security automation.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredML has been used mainly for intrusion detection, while DL for malware and anomalies detection in IoT security.
“Among the potential surveyed applications, ML has been used mainly for intrusion detection, while DL for malware and anomalies detection.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
The Network layer section describes ML-based intrusion detection, while the Application layer describes DL-based malware detection and the RBMs section says deep models can detect network anomalies (page numbers unavailable in our copy). These examples support the applications named, but the survey also describes ML-based anomaly detection and does not establish that ML was used mainly for intrusion detection or DL mainly for malware and anomaly detection. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe review is primarily focused on IoT environments and does not directly address the integration of ML/DL into DevSecOps pipelines, which may limit its relevance for general DevSecOps practices.
“The review is primarily focused on IoT environments and does not directly address the integration of ML/DL into DevSecOps pipelines, which may limit its relevance for general DevSecOps practices.”
From Evolution of DevSecOps and Its Influence on Application Security: A Systematic Literature Review (2025) · cites this paper as [32]
There is a need for diverse datasets within IoT security.
“As main conclusions, the authors claim the need for diverse datasets within IoT security.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
Several security aspects shall be considered in every IoT system.
“Several security aspects shall be considered in every IoT system (Al-Garadi et al. 2020):”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
IoT systems need to move from simply enabling secure communication between devices to intelligence provided by DL approaches to create robust, all-encompassing security solutions.
“For all of these explanations, IoT systems need to go from simply enabling secure communication between devices to intelligence provided by DL approaches to create robust, all-encompassing security solutions, as noted by the al2020survey.”
From Deepfakes: current and future trends (2024) · cites this paper as Al-Garadi et al. 2020
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“insights from these data, researchers [51]–[57] have discussed different methods of integrating big data analytical methods with IoT design.”
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A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks
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IoT Goes Nuclear: Creating a ZigBee Chain Reaction
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DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
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Practical Evasion of a Learning-Based Classifier: A Case Study
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- Authors
- Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M.
- Affiliations
- Qatar University (QA)
- Temple University (US)
- University of Malaya (MY)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 3
- Pages
- 1646-1685
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2885012953
- Semantic Scholar
- da8a949f9c9f1df3a38f12c2cac97b789c705465
- Cited by
- 1,392
- References
- 294
- Citations by year
- 2026: 170 · 2025: 234 · 2024: 232 · 2023: 237 · 2022: 213 · 2021: 217 · 2020: 75 · 2019: 13 · 2018: 1
- FWCI
- 87.17
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NPRP from the Qatar National Research Fund (8-408-2-172, 12S-0305-190231)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things security, machine learning for security, deep learning for security, IoT security threats, attack surface, IoT vulnerabilities, intrusion detection, anomaly detection, authentication, access control, network security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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